Load dataset: This has already had exclusions applied to it, it is a complete case dataset

Demographics of dataset following exclusions

Age

Age has already been limited to age 35-65

Sex

Ethnicity

Ethnicity has multiple catagoies in UK Biobank, these have been recoded to groupings that are most relevant to bone health.

Ethnicity was recoded to show the following groupings - 1 = White, - 2 = Asian and British Asian, - 3 = Black and Black British, - 4 = Mixed

Exposure variables (PA)

Participants were asked about activities undertaken in the last 4 weeks including frequency, intensity and duration. These are reported as raw values. From these a number of derived Metabolic Equivalent Task (MET) minutes of activity were also derived.

We have added a derived value of mins/wk spent in each activity for use in comparing to the accelerometry data later.

Activity measures (split into moderate, vigorous, walking and summed activity)

number of days of activity per week
duration of activity per week
mins/wk of activity
MET mins of activity per week

Treatment of outliers (already applied to the data by UKB)

For individual catagories of walking, moderate and vig activity exercise was capped at a maximum of 180 minutes a day, allowing a maximum of 21 hours a week (3 hours*7)
Extreme outliers where the sum of walking, moderate and vigorous time variables exceeds 960 minutes (16 hours a day) the whole record is excluded from the analysis as unlikely.

Missing data

We are using only participants with all of duration and number of days/wk of activity in our main analysis. Those with missing values in either column are considered NA in the MET calculations and mins/wk calculations. This differs to the UKB anaysis where some data is imputed if just duration is missing. We will use this for comparison in the sensitivity analysis.

Unable to walk

For those who respond that they were unable to walk instead of removing them they are given a value of 0 under time spent walking.

Derivation and cutpoints

The full IPAQ calculates total physical activity as a combination of both leisure time PA (LTPA) as well as domains of work and normal activity - as such the resulting median MET-minutes is higher than for participation in LTPA alone. The general public health recommendations of 30 mins of MVPA are relatively low with most adults being able to achieve this regardless of leisure time activity. To look at the health benefits of LTPA higher cut point thresholds are needed for UKB IPAQ data.

Moderate activity

## Saved: ../results/tables/Table_3.1.docx

sex	Variable	N	Mean	SD	Median	Max	IQR_low	IQR_high
Female	mins_wk_mod	171,043	248.06	404.29	120	8,820	30	300
Female	MET_mod	171,043	992.22	1,617.15	480	35,280	120	1,200
Male	mins_wk_mod	151,710	281.74	493.26	105	10,080	30	300
Male	MET_mod	151,710	1,126.95	1,973.04	420	40,320	120	1,200
Total	mins_wk_mod	322,753	263.89	448.63	120	10,080	30	300
Total	MET_mod	322,753	1,055.55	1,794.51	480	40,320	120	1,200

Vigorous activity

## Saved: ../results/tables/Table_3.2.docx

sex	Variable	N	Mean	SD	Median	Max	IQR_high
Female	mins_wk_vig	171,043	75.97	149.25	24	8,400	90
Female	MET_vig	171,043	607.75	1,194.03	192	67,200	720
Male	mins_wk_vig	151,710	112.74	240.98	40	7,560	125
Male	MET_vig	151,710	901.91	1,927.85	320	60,480	1,000
Total	mins_wk_vig	322,753	93.25	198.59	30	8,400	120
Total	MET_vig	322,753	746.02	1,588.74	240	67,200	960

Walking behaviour

## Saved: ../results/tables/Table_3.3.docx

sex	Variable	N	Mean	SD	Median	Max	IQR_low	IQR_high
Female	mins_wk_walk	171,043	359.38	505.59	210	10,080	100	420
Female	MET_walk	171,043	1,185.94	1,668.45	693	33,264	330	1,386
Male	mins_wk_walk	151,710	381.53	567.80	210	8,750	90	420
Male	MET_walk	151,710	1,259.05	1,873.73	693	28,875	297	1,386
Total	mins_wk_walk	322,753	369.79	535.84	210	10,080	90	420
Total	MET_walk	322,753	1,220.31	1,768.29	693	33,264	297	1,386

Combined activity

MVPA This variable has been derived along with mins/wk in MVPA in order to compare to other published data and the accelerometry data

## Saved: ../results/tables/Table_3.4.docx

sex	Variable	N	Mean	SD	Median	Max	IQR_low	IQR_high
Female	mins_wk_MVPA	171,043	324.02	481.23	175	10,920	60	400
Female	MET_MVPA	171,043	1,599.97	2,346.96	840	77,280	240	2,000
Male	mins_wk_MVPA	151,710	394.47	643.13	180	15,120	50	440
Male	MET_MVPA	151,710	2,028.85	3,346.82	960	90,720	240	2,400
Total	mins_wk_MVPA	322,753	357.14	564.25	180	15,120	55	420
Total	MET_MVPA	322,753	1,801.57	2,868.80	920	90,720	240	2,160

Summed days activity per week

Sum of days performing walking, moderate and vigorous activity - question derived from answers to number of days doing each of the following in a week so total of all will add up to more than 7 but cant be over 21.

Summed MET minutes per week for all activity

As with the above this is the total amount of time in minutes spent walking, doing vigorous and doing moderate PA. Unlike summed days this should not exceed the total number of minutes in a week

## Saved: ../results/tables/Table_3.3.docx

sex	Variable	N	Mean	SD	Median	Max	IQR_low	IQR_high
Female	summed_MET_all	171,043	2,785.92	3,398.64	1,734	80,052	812	3,408
Male	summed_MET_all	151,710	3,287.90	4,585.45	1,824	115,668	813	3,786
Total	summed_MET_all	322,753	3,021.88	4,008.42	1,773	115,668	813	3,573

International Physical Activity Questionnaire (IPAQ)

Based on activity frequency, duration and intensity an activity group is derived as being low, moderate or high active.

Physical activity status by outcome

## Saved: ../results/tables/Table_3.5.docx

Fracture status	Variable	N	Mean	SD	Median	Min	Max	IQR_low	IQR_high
No	MET_mod	292,665	1,040.97	1,776.11	420	0	40,320	120	1,200
No	MET_vig	292,665	732.44	1,565.01	240	0	67,200	0	960
No	MET_MVPA	292,665	1,773.41	2,830.07	900	0	80,640	240	2,160
No	MET_walk	292,665	1,209.39	1,751.58	693	0	33,264	297	1,386
No
Yes	MET_mod	30,088	1,197.37	1,958.92	480	0	30,240	160	1,440
Yes	MET_vig	30,088	878.11	1,798.05	320	0	60,480	0	1,080
Yes	MET_MVPA	30,088	2,075.48	3,208.53	1,080	0	90,720	280	2,520
Yes	MET_walk	30,088	1,326.53	1,919.99	693	0	28,875	330	1,386
Yes
	MET_mod	322,753	1,055.55	1,794.51	480	0	40,320	120	1,200
	MET_vig	322,753	746.02	1,588.74	240	0	67,200	0	960
	MET_MVPA	322,753	1,801.57	2,868.80	920	0	90,720	240	2,160
	MET_walk	322,753	1,220.31	1,768.29	693	0	33,264	297	1,386

Visual comparison (box plots)

Moderate PA

Vigorous PA

#### MVPA

Relationship between moderate and vigorous PA

Log transformations to correct for extreme skew

All data is skewed, look at whether log transformation helps normalise the data

examine for variation at extremes of activity

## # A tibble: 4 × 4
##   MET_mod_bin      N Fractures `Fracture %`
##   <fct>        <int>     <int>        <dbl>
## 1 0–150 min    86203      7376         8.56
## 2 151–300 min  46941      4070         8.67
## 3 301–450 min  27243      2359         8.66
## 4 >450 min    159393     15911         9.98

For all variables - need to consider the split of PA

## Warning: Use of .data in tidyselect expressions was deprecated in tidyselect 1.2.0.
## ℹ Please use `all_of(var)` (or `any_of(var)`) instead of `.data[[var]]`
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## # A tibble: 16 × 5
##    PA_variable Bin              N Fractures `Fracture %`
##    <chr>       <fct>        <int>     <int>        <dbl>
##  1 MET_MVPA    0–150 min    63357      5445         8.59
##  2 MET_MVPA    151–300 min  26798      2261         8.44
##  3 MET_MVPA    301–450 min  19322      1650         8.54
##  4 MET_MVPA    >450 min    210303     20360         9.68
##  5 MET_mod     0–150 min    86203      7376         8.56
##  6 MET_mod     151–300 min  46941      4070         8.67
##  7 MET_mod     301–450 min  27243      2359         8.66
##  8 MET_mod     >450 min    159393     15911         9.98
##  9 MET_vig     0–150 min   138423     12146         8.77
## 10 MET_vig     151–300 min  31411      2808         8.94
## 11 MET_vig     301–450 min  16863      1502         8.91
## 12 MET_vig     >450 min    133083     13260         9.96
## 13 MET_walk    0–150 min    41212      3644         8.84
## 14 MET_walk    151–300 min  41319      3555         8.6 
## 15 MET_walk    301–450 min  32474      2834         8.73
## 16 MET_walk    >450 min    204775     19683         9.61

#### Extreme behaviour

## # A tibble: 12 × 5
##    Bin             N Fractures `Fracture %` PA_variable
##    <fct>       <int>     <int>        <dbl> <chr>      
##  1 Bottom 10%  39733      3499         8.81 MET_MVPA   
##  2 Middle 80% 248214     22545         9.08 MET_MVPA   
##  3 Top 10%     31833      3672        11.5  MET_MVPA   
##  4 Bottom 10%  46092      4026         8.73 MET_mod    
##  5 Middle 80% 243519     22408         9.2  MET_mod    
##  6 Top 10%     30169      3282        10.9  MET_mod    
##  7 Bottom 10% 118157     10397         8.8  MET_vig    
##  8 Middle 80% 175003     16116         9.21 MET_vig    
##  9 Top 10%     26620      3203        12.0  MET_vig    
## 10 Bottom 10%  36806      3278         8.91 MET_walk   
## 11 Middle 80% 257881     23791         9.23 MET_walk   
## 12 Top 10%     25093      2647        10.6  MET_walk

Examined by 10 year age group

## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0'. You can override using the
## `.groups` argument.

## # A tibble: 36 × 6
## # Groups:   agegp_A0 [3]
##    agegp_A0 Bin            N Fractures `Fracture %` PA_variable
##    <fct>    <fct>      <int>     <int>        <dbl> <chr>      
##  1 40-49    Bottom 10% 10729       828         7.72 MET_MVPA   
##  2 40-49    Middle 80% 71137      6410         9.01 MET_MVPA   
##  3 40-49    Top 10%     8590      1149        13.4  MET_MVPA   
##  4 50-59    Bottom 10% 16825      1504         8.94 MET_MVPA   
##  5 50-59    Middle 80% 96403      8538         8.86 MET_MVPA   
##  6 50-59    Top 10%    11391      1251        11.0  MET_MVPA   
##  7 60-69    Bottom 10% 12179      1167         9.58 MET_MVPA   
##  8 60-69    Middle 80% 80674      7597         9.42 MET_MVPA   
##  9 60-69    Top 10%    11852      1272        10.7  MET_MVPA   
## 10 40-49    Bottom 10% 13247      1048         7.91 MET_mod    
## # ℹ 26 more rows

#### age and sex

## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.
## `summarise()` has grouped output by 'agegp_A0', 'sex'. You can override using
## the `.groups` argument.

## # A tibble: 72 × 7
## # Groups:   agegp_A0, sex [6]
##    agegp_A0 sex    Bin            N Fractures `Fracture %` PA_variable
##    <fct>    <fct>  <fct>      <int>     <int>        <dbl> <chr>      
##  1 40-49    Female Bottom 10%  6123       404         6.6  MET_MVPA   
##  2 40-49    Female Middle 80% 39239      2789         7.11 MET_MVPA   
##  3 40-49    Female Top 10%     3621       374        10.3  MET_MVPA   
##  4 40-49    Male   Bottom 10%  4606       424         9.21 MET_MVPA   
##  5 40-49    Male   Middle 80% 31898      3621        11.4  MET_MVPA   
##  6 40-49    Male   Top 10%     4969       775        15.6  MET_MVPA   
##  7 50-59    Female Bottom 10%  9220       897         9.73 MET_MVPA   
##  8 50-59    Female Middle 80% 53376      4905         9.19 MET_MVPA   
##  9 50-59    Female Top 10%     5164       560        10.8  MET_MVPA   
## 10 50-59    Male   Bottom 10%  7605       607         7.98 MET_MVPA   
## # ℹ 62 more rows

Cross Sectional data descriptive

Catherine Rolls

2026-02-02